ArticleNeuroImage2018
Multi-subject hierarchical inverse covariance modelling improves estimation of functional brain networks.
Article in NeuroImage, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- MFCSC: Novel method to calculate mismatch between functional and structural brain connectomes, and its application for detecting hemispheric functional specialisations.Scientific reports · 2023Article
- Single-index models with functional connectivity network predictors.Biostatistics (Oxford, England) · 2022Article
- Modelling subject variability in the spatial and temporal characteristics of functional modes.NeuroImage · 2020Article
- Optimising network modelling methods for fMRI.NeuroImage · 2020Article
- Analysing brain networks in population neuroscience: a case for the Bayesian philosophy.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2020Article
- Joint Bayesian-Incorporating Estimation of Multiple Gaussian Graphical Models to Study Brain Connectivity Development in Adolescence.IEEE transactions on medical imaging · 2020Article
- A Tensor-Based Framework for rs-fMRI Classification and Functional Connectivity Construction.Frontiers in neuroinformatics · 2020Article
- Weighted Graph Regularized Sparse Brain Network Construction for MCI Identification.Pattern recognition · 2019Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
A Bayesian model for sparse, hierarchical, inver-covariance estimation is presented, and applied to multi-subject functional connectivity estimation in the human brain. It enables simultaneous inference of the strength of connectivity between brain regions at both subject and population level, and is applicable to fMRI, MEG and EEG data. Two versions of the model can encourage sparse connectivity, either using continuous priors to suppress irrelevant connections, or using an explicit description of the network structure to estimate the connection probability between each pair of regions. A large evaluation of this model, and thirteen methods that represent the state of the art of inverse covariance modelling, is conducted using both simulated and resting-state functional imaging datasets. Our novel Bayesian approach has similar performance to the best extant alternative, Ng et al.'s Sparse Group Gaussian Graphical Model algorithm, which also is based on a hierarchical structure. Using data from the Human Connectome Project, we show that these hierarchical models are able to reduce the measurement error in MEG beta-band functional networks by 10%, producing concomitant increases in estimates of the genetic influence on functional connectivity.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.